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dc.contributor.authorVan der Westhuizen, Magderie
dc.contributor.authorHattingh, Giel
dc.contributor.authorKruger, Hennie
dc.date.accessioned2014-07-24T12:22:22Z
dc.date.available2014-07-24T12:22:22Z
dc.date.issued2010
dc.identifier.citationVan der Westhuizen, M. et al. 2010. Experiments to improve forecasting accuracy of regression models with minimal assumptions. Lecture notes in management science, 2:34-44. [http://orlabanalytics.ca/lnms/archive/v2/lnmsv2p34.pdf]en_US
dc.identifier.issn2008-0050
dc.identifier.issn1927-0097 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/10896
dc.identifier.urihttp://orlabanalytics.ca/lnms/archive/v2/lnmsv2p34.pdf
dc.description.abstractThe forecasting accuracy of a regression model relies heavily on the applicability of the assumptions made by the model builder. In addition, the presence of outliers may also lead to models that are not reliable and thus less robust. In this paper a suggested regression model, based on minimal assumptions, is studied and extended in an effort to improve forecast accuracy. The approach is based on mathematical programming techniques combined with smoothing and piecewise linear techniques. Three cases from the literature are considered and presented as illustrative examples.en_US
dc.language.isoenen_US
dc.publisherORLab Analyticsen_US
dc.subjectRobust modelsen_US
dc.subjectoutlier detectionen_US
dc.subjectpiecewise linear regressionen_US
dc.titleExperiments to improve forecasting accuracy of regression models with minimal assumptionsen_US
dc.typeArticleen_US
dc.contributor.researchID12066621 - Kruger, Hendrik Abraham
dc.contributor.researchID10170758 - Hattingh, Johannes Michiel


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